Mixed heuristic-non linear optimization of energy management for hydrogen storage-based multi carrier hubs

Rosario Miceli, Gaetano Zizzo, Eleonora Riva Sanseverino, Diego La Cascia, Massimo Bertoncini, Diego Arnone, Rosario Proietto, Alessandro Rossi

Risultato della ricerca: Paper

11 Citazioni (Scopus)

Abstract

In this paper, an heuristic and non-linear programming based algorithm to optimally operate an energy hub plant is proposed. The energy hub plant described in this work is the test system for the European INGRID research project. The Energy Management System defines the optimal energy flows dispatch in order to obtain the energy balance and the maximum profit for the owner of the plant. The problem is highly constrained and non-linear, for this reason the methodology cannot rely on Linear Programming (LP) methods. The Energy Management System manages two energy carriers, electricity and hydrogen, interfacing three distribution networks: the electricity, the hydrogen and the methane networks. Simulations show that the buffer function of the system is as more intense as greater the efficiency of the conversion systems is, as compared to the prices variations along the day. Although heuristic search methods are well-suited for the solution of highly constrained non-linear problems, the applications carried out over the INGRID project test-bed show that improved solutions can be found applying a non-linear programming method named Generalised Reduced Gradient (GRG), to refine the solutions outputted by heuristic algorithms, such as Simulated Annealing or Tabu Search.
Lingua originaleEnglish
Stato di pubblicazionePublished - 2014

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Energy management
Hydrogen storage
Energy management systems
Nonlinear programming
Heuristic programming
Electricity
Hydrogen
Tabu search
Heuristic algorithms
Energy balance
Simulated annealing
Electric power distribution
Linear programming
Profitability
Methane

All Science Journal Classification (ASJC) codes

  • Energy Engineering and Power Technology
  • Fuel Technology

Cita questo

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title = "Mixed heuristic-non linear optimization of energy management for hydrogen storage-based multi carrier hubs",
abstract = "In this paper, an heuristic and non-linear programming based algorithm to optimally operate an energy hub plant is proposed. The energy hub plant described in this work is the test system for the European INGRID research project. The Energy Management System defines the optimal energy flows dispatch in order to obtain the energy balance and the maximum profit for the owner of the plant. The problem is highly constrained and non-linear, for this reason the methodology cannot rely on Linear Programming (LP) methods. The Energy Management System manages two energy carriers, electricity and hydrogen, interfacing three distribution networks: the electricity, the hydrogen and the methane networks. Simulations show that the buffer function of the system is as more intense as greater the efficiency of the conversion systems is, as compared to the prices variations along the day. Although heuristic search methods are well-suited for the solution of highly constrained non-linear problems, the applications carried out over the INGRID project test-bed show that improved solutions can be found applying a non-linear programming method named Generalised Reduced Gradient (GRG), to refine the solutions outputted by heuristic algorithms, such as Simulated Annealing or Tabu Search.",
author = "Rosario Miceli and Gaetano Zizzo and {Riva Sanseverino}, Eleonora and {La Cascia}, Diego and Massimo Bertoncini and Diego Arnone and Rosario Proietto and Alessandro Rossi",
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TY - CONF

T1 - Mixed heuristic-non linear optimization of energy management for hydrogen storage-based multi carrier hubs

AU - Miceli, Rosario

AU - Zizzo, Gaetano

AU - Riva Sanseverino, Eleonora

AU - La Cascia, Diego

AU - Bertoncini, Massimo

AU - Arnone, Diego

AU - Proietto, Rosario

AU - Rossi, Alessandro

PY - 2014

Y1 - 2014

N2 - In this paper, an heuristic and non-linear programming based algorithm to optimally operate an energy hub plant is proposed. The energy hub plant described in this work is the test system for the European INGRID research project. The Energy Management System defines the optimal energy flows dispatch in order to obtain the energy balance and the maximum profit for the owner of the plant. The problem is highly constrained and non-linear, for this reason the methodology cannot rely on Linear Programming (LP) methods. The Energy Management System manages two energy carriers, electricity and hydrogen, interfacing three distribution networks: the electricity, the hydrogen and the methane networks. Simulations show that the buffer function of the system is as more intense as greater the efficiency of the conversion systems is, as compared to the prices variations along the day. Although heuristic search methods are well-suited for the solution of highly constrained non-linear problems, the applications carried out over the INGRID project test-bed show that improved solutions can be found applying a non-linear programming method named Generalised Reduced Gradient (GRG), to refine the solutions outputted by heuristic algorithms, such as Simulated Annealing or Tabu Search.

AB - In this paper, an heuristic and non-linear programming based algorithm to optimally operate an energy hub plant is proposed. The energy hub plant described in this work is the test system for the European INGRID research project. The Energy Management System defines the optimal energy flows dispatch in order to obtain the energy balance and the maximum profit for the owner of the plant. The problem is highly constrained and non-linear, for this reason the methodology cannot rely on Linear Programming (LP) methods. The Energy Management System manages two energy carriers, electricity and hydrogen, interfacing three distribution networks: the electricity, the hydrogen and the methane networks. Simulations show that the buffer function of the system is as more intense as greater the efficiency of the conversion systems is, as compared to the prices variations along the day. Although heuristic search methods are well-suited for the solution of highly constrained non-linear problems, the applications carried out over the INGRID project test-bed show that improved solutions can be found applying a non-linear programming method named Generalised Reduced Gradient (GRG), to refine the solutions outputted by heuristic algorithms, such as Simulated Annealing or Tabu Search.

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